Despite advances in cellular network technology, base station (BS) load
balancing remains a persistent problem. Although centralized resource
allocation methods can address the load balancing problem, it still remains an
NP-hard problem. In this research, we study how federated deep Q learning can
be used to inform each user equipment (UE) of the each BS's load conditions.
Federated deep Q learning's load balancing enables intelligent UEs to
independently select the best BS while also limiting the amount of private
information exposed to the network.
In this study, we propose and analyze a federated deep Q learning load
balancing system, which is implemented using the Open-RAN xAPP framework and
the near-Real Time Radio Interface Controller (near-RT RIC). Our simulation
results indicate that compared to the maximum Signal-To-Noise-Ratio (MAX-SINR)
method currently used by UEs, our proposed deep Q learning model can
consistently provide better High average UE quality of service

本研究提出并分析了一种基于联邦深度 Q 学习的负载均衡系统，该系统使用 Open-RAN xAPP 框架和近实时无线接口控制器 (near-RT RIC) 进行实现。模拟结果表明，与当前由用户设备使用的最大信噪比 (MAX-SINR) 方法相比，我们提出的深度 Q 学习模型能够持续提供更好的用户设备服务质量。

联邦深度 Q-Learning 与 5G 负载均衡

Federated Deep Q-Learning and 5G load balancing

Random Access is an important step in enabling the initial attachment of a
User Equipment (UE) to a Base Station (gNB). The UE identifies itself by
embedding a Preamble Index (RAPID) in the phase rotation of a known base
sequence, which it transmits on the Physical Random Access Channel (PRACH). The
signal on the PRACH also enables the estimation of propagation delay, often
known as Timing Advance (TA), which is induced by virtue of the UE's position.
Traditional receivers estimate the RAPID and TA using correlation-based
techniques. This paper presents an alternative receiver approach that uses
AI/ML models, wherein two neural networks are proposed, one for the RAPID and
one for the TA. Different from other works, these two models can run in
parallel as opposed to sequentially. Experiments with both simulated data and
over-the-air hardware captures highlight the improved performance of the
proposed AI/ML-based techniques compared to conventional correlation methods.

这篇论文介绍了一种使用人工智能 / 机器学习模型的替代接收器方法，其中提出了两个神经网络分别用于 RAPID 和 TA 的估计，通过模拟数据和硬件捕获实验证明了与传统相关方法相比，所提出的基于人工智能和机器学习技术的方法具有更好的性能。